Comparative Study Between Internal Ohmic Resistance and Capacity for Battery State of Health Estimation

M. Nisvo Ramadan, Bhisma Adji Pramana, Sigit Agung Widayat, Lora Khaula Amifia, Adha Cahyadi, Oyas Wahyunggoro

Abstract

In order to avoid battery failure, a battery management system (BMS) is necessary. Battery state of charge (SOC) and state of health (SOH) are part of information provided by a BMS. This research analyzes methods to estimate SOH based lithium polymer battery on change of its internal resistance and its capacity. Recursive least square (RLS) algorithm was used to estimate internal ohmic resistance while coloumb counting was used to predict the change in the battery capacity. For the estimation algorithm, the battery terminal voltage and current are set as the input variables. Some tests including static capacity test, pulse test, pulse variation test and before charge-discharge test have been conducted to obtain the required data. After comparing the two methods, the obtained results show that SOH estimation based on coloumb counting provides better accuracy than SOH estimation based on internal ohmic resistance. However, the SOH estimation based on internal ohmic resistance is faster and more reliable for real application



Keywords


battery management system; state of health; lithium polymer; recursive least square; coulomb counting

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References


S. Cole and M. Leslie, “Long-term global warming trend sustained in 2013,? 2014. [Online]. Available: http://climate.nasa.gov/news/1029/.

A. Dinger et al., and Xavier, “Focus Batteries for Electric Cars,? 2010.

T. Sar et al., “A Hybrid Battery Model and State of Health Estimation Method for Lithium-Ion Batteries,? pp. 1349–1356, 2014. crossref

S. Li et al., “Study of Battery Modeling using Mathematical and Circuit Oriented Approaches,? pp. 1–8, 2011. crossref

H. Lin et al., “Estimation of Battery State of Health Using Probabilistic Neural Network,? vol. 9, no. 2, pp. 679–685, 2013. crossref

D. Andre et al., “Comparative study of a structured neural network and an extended Kalman filter for state of health determination of lithium-ion batteries in hybrid electric vehicles,? Eng. Appl. Artif. Intell., vol. 26, no. 3, pp. 951–961, 2013. crossref

P. Singh and D. Reisner, “Fuzzy logic-based state-of-health determination of lead acid batteries,? 24th Annu. Int. Telecommun. Energy Conf., pp. 583–590. crossref

V. Klass and M. Behm, “A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation,? vol. 270, pp. 262–272, 2014. crossref

Z. Guo et al., “State of health estimation for lithium ion batteries based on charging curves,? J. Power Sources, vol. 249, pp. 457–462, 2014. crossref

Y. Zou et al., “Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles,? J. Power Sources, vol. 273, pp. 793–803, 2015. crossref

C. R. Gould et al., “New Battery Model and State-of-Health Determination Through Subspace Parameter Estimation and State-Observer Techniques,? vol. 58, no. 8, pp. 3905–3916, 2009. crossref

A. Hentunen et al., “Electrical battery model for dynamic simulations of hybrid electric vehicles,? 2011 IEEE Veh. Power Propuls. Conf., pp. 1–6, Sep. 2011. crossref

X. Li and S. Choe, “State-of-charge (SOC) estimation based on a reduced order electrochemical thermal model and extended Kalman filter,? 2013 Am. Control Conf., pp. 1100–1105, Jun. 2013. crossref

X. Hu et al., “A comparative study of equivalent circuit models for Li-ion batteries,? J. Power Sources, vol. 198, pp. 359–367, 2012. crossref

L. Lu et al., “A review on the key issues for lithium-ion battery management in electric vehicles,? J. Power Sources, vol. 226, pp. 272–288, Mar. 2013. crossref

A. Hand, “No Title.? [Online]. Available: http://www.hobbyking.com/hobbyking/store/__317__85__Batteries_Accessories-Turnigy_Lipoly.html. [Accessed: 20-Nov-2015].

A. Zenati et al., “A Methodology to Assess the State Of Health of Lithium-ion Batteries Based on the Battery ’ s Parameters and a Fuzzy Logic System,? IECON 2010, 2010. crossref

E. Davis et al., “Internal ohmic measurements and their relationship to battery capacity – epri’s ongoing technology evaluation,? pp. 1–10.

M. U. Cuma and T. Koroglu, “A comprehensive review on estimation strategies used in hybrid and battery electric vehicles,? Renew. Sustain. Energy Rev., vol. 42, pp. 517–531, 2015. crossref

J. Remmlinger et al., “On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models q,? J. Power Sources, vol. 239, pp. 689–695, 2013. crossref


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